Super-Consumer Based Reputation Management for Web Service Systems

Transcription

1 Super-Consumer Based Reputation Management for Web Service Systems Yao Wang, Julita Vassileva Department of Computer Science, University of Saskatchewan, Canada, {yaw181, Abstract In this paper, we propose a mechanism of using super consumers to manage reputation. Super consumers are consumers with more capabilities, such as cpu power, storage and bandwidth. Super consumers can serve as reputation managers to build global reputation for a service based on the opinions from all the members in the system. Super consumers can also build community-based reputation for a service, by forming their own communities based on similar interests and judgement criteria. Our experiments show that the system using both global and community-based reputation can perform better than the system using just global reputation. Index Terms reputation, super consumers, web services. I. INTRODUCTION Web services provide a flexible way for applications to interact with each other over networks. The flexibility is represented in two aspects. The language-/platform- independence allows applications to invoke each other s services, no matter what platforms or languages they are using. A web service is also self-describing so that applications can examine the functionality of a web service at runtime and generate corresponding code to automatically invoke the service. The flexibility has made web services well accepted and seen as a promising solution for system integration. A typical architecture of web services uses a central UDDI server for service providers to publish their services and for service consumers to search services. There is no doubt that the centralized method is simple and efficient, however, it inherently bears the problem of a single point of failure. Moreover, the information stored in the UDDI server may become outdated in a dynamic networking environment where a service may become unreachable. Therefore, decentralized web service systems have been proposed [2, 4, 10], which can be based on Gnutella protocol or super-peer networks. No matter whether web service systems are centralized or decentralized, they are open systems where some services are of high quality, and some not, where some providers are honest and provide services as they advertise, and others are not. Trust and reputation mechanisms are important to distinguish good from bad services and good service providers from bad ones. Further on we will use the term trust to refer to an individual (subjective, based on individual experience) representation of a service s quality, reliability, honesty etc., and the term reputation to refer a collective (based on a group / community experience) representation. Reputation approaches applied in centralized systems are different from those applied in decentralized systems. In a centralized system, like ebay, a central node is responsible for collecting feedbacks (e.g. ratings) from consumers and building reputation for providers and/or consumers. The centralized method is straightforward and easy to implement, but requires a powerful and reliable central server and a lot of bandwidth for computation, data storage, and communication. In a decentralized system, the members in the system have to cooperate and share the responsibilities to manage reputation. Although a decentralized approach solves some of the problems of centralized approaches, such as single point of failure and scalability, it has higher costs for computation and communication. For instance, in Yu and Singh s approach [15], in order to know the reputation of a peer A, a peer B has to seek the opinions of many other peers about A and combine these opinions. If a peer C also wants to know A s reputation, it has to repeat the same process as B, which leads to redundant communication and computation cost. Another weakness of a decentralized method is that it may be hard for a peer to collect information from other peers about their opinions of a given peer. One reason is that the information about each individual peer can be distributed anywhere in a system. A peer may not be able to find it, since it doesn t know which other peers have interacted with the peer for which it is trying to build reputation. Peers can be online or offline at any time, so the opinions of those that are offline at the moment of request will not be considered. For these reasons the peer s reputation may not be reliable. Besides, in a decentralized system, especially a peer-to-peer system, peers are supposed to have equal roles and responsibilities no matter what their capabilities are, for instance, their connection bandwidth, cpu processing power, or availability. However, it has been shown [9] that in practice there is a great heterogeneity in the capability of peers. Peers with poor capabilities become bottlenecks and degrade the system performance. This leads to the appearance of super-peer networks, such as Kazaa, where super-peers take some server responsibilities. Super-peers are peers with more capabilities, such as cpu power, bandwidth and availability, that are mainly used to facilitate the task of searching resources [14]. We exploit the idea of super-peers to manage reputation. Our approach bears the characteristics of both centralized and decentralized reputation systems, but overcomes their drawbacks. It can be applied to decentralized web service systems, for example, systems based on Gnutella networks or super-peer networks. It can even work under the typical web service architecture, which uses a centralized UDDI server for finding services. The rest of this paper is organized as follows: section II presents our approach of using super-consumers to manage reputation. The experiments and results are described in

2 section III.. Section IV discusses some related work and possible directions for future work. The last section V presents the conclusion. II. SUPER-CONSUMER BASED REPUTATION MANAGEMENT A. Overview In a web service system, the peers are service providers or service consumers. Super-consumers are both service consumers that have a fast cpu processing power, high bandwidth and high availability, i.e. equivalent to super-peers. Super-consumers are responsible for building and managing reputation for web services. There are two ways for consumers to get reputation information of services, through services reputation managers and/or through communities. The reputation managers of a service are super-consumers who manage reputation information for the service. When a service provider publishes a new service, it needs to tell its consumers not only how to invoke the service through the WSDL document, but also where to find the service s reputation information. Therefore, before publishing the service, the service provider has to find several super-consumers who would like to manage the reputation for the service. These super consumers become the service s reputation managers. The responsibility of being a service s reputation manager includes collecting feedback from consumers who have interacted with the service, building reputation representation for the service based on consumers feedbacks, and providing the service s reputation information for other consumers. The reputation managers of a given service act independently and provide redundant reputation information. In this way loss of information is prevented in case one of them fails or is temporarily inaccessible. When a super consumer receives from a service provider a request to be the reputation manager for a service, it will check whether it is interested in this kind of. For example, a consumer who likes travelling may have interest the services that provide weather forecast. If a super-consumer is interested in the service, it will accept the request and become the service s reputation manager. If the super-consumer is not interested in the service or is already the reputation manager for too many other services, it can refuse the request. After a service provider finds several reputation managers for its service, it will publish the information so that other consumers know where to find the service s reputation information or where to send their feedback. After a consumer has utilized a service, it will send its feedbacks about the service to all the reputation managers of the service. Each reputation manager of the service will calculate the reputation of the service according to received feedbacks independently (see section C). Fig. 1 shows the procedure of how a consumer acquires a service s reputation information from one of the reputation managers of the service. Step 1. A service provider sends requests to super consumers to find reputation managers for its service. Step 2. A consumer can search for a service by keywords. The search method depends on the underlying network. If it is a Gnutella-based or super-peer network, it uses the flooding algorithm. If it is based on the typical web service architecture, the consumer can find services from the central UDDI server. If there is a match, the consumer can know not only what the matched service is, but also who are the service s reputation managers. Step 3. After finding a matched service, a consumer can randomly select a reputation manager of the service and send to it a request to get the reputation of the service. Step 4. If the consumer decides to select the service, it can invoke it. Otherwise, it will repeat steps 2 and 3 to select another service. Step 5. After consuming a service, the consumer will send its feedback to all the reputation managers of the service so that they can update the reputation of the service. A super consumer can gossip periodically with other super consumers who share the same interest to exchange the reputation information they have. For example, a reputation manager for a weather service A can gossip with another reputation manager of a weather service B so that both reputation managers can know the reputation of both the weather service A and B. In this way, when a consumer is looking for a weather service, it can know reputations for a group of weather services from any reputation manager of a weather service that gossips with other such reputation managers. There are two ways for a reputation manager to find other reputation managers with the same interest to exchange information. One way is to send a search request to find other reputation managers with the same interest identified by keywords, like weather service. The other way is by gossiping. When different reputation managers gossip with each other, they also exchange information about other reputation managers they know. So by gossiping, a reputation manager can gradually know more other reputation managers with the same interest. Finally, a reputation manager can build an overview of the reputation of the services that provide the same or similar functionalities from other reputation managers so that a consumer can compare these services and select one that is the best for it. In our mechanism, a super-consumer that serves as a reputation manager for a given service can build two kinds of reputation for the service. When the reputation is built using the feedbacks from all the consumers, it is called the global reputation of the service. The other kind of reputation is called community-based reputation and it is built using only the feedback from selected consumers. A super-consumer can build an implicit community of customers sharing its own interests and judging criteria. If it does, the super-consumer becomes the administrator of its community. It selects the members of the community based on the trust it has developed in them. The process of building trust among consumers (including super-consumers) is explained below.

3 C 5 Feedback 5 Feedback SC1 3 Reputation2 Search 4 Invoke SC2 SC3 C Consumer SC1 1 Request / Reply(yes) WS 1 Request / Reply(yes) 1 Request / Reply(no) Super Consumer WS1 Fig. 1 The procedure of finding reputation for a service Web Service Consumers build trust in each other by comparing their feedback about the same service. The trust of one consumer in another consumer is used to measure the similarity in judging criteria between the trusting and trusted consumer so that the first can trust opinions from the second consumer. A reputation manager of a service stores all the feedbacks about the interactions with the service that happened in the current time period. Since a consumer sends its feedback to all the reputation managers of a service, the reputation managers of a service keep the same feedbacks. After using a service, a consumer can build trust in other consumers by comparing its feedback with other consumers feedback. A super consumer can build multiple communities based on its different interests in services, quality requirements and ways of judging services. An interest can be defined by keywords, such as weather service that represents an interest in a kind of services that provide weather forecast. Two communities built by different super consumers may have different community-based reputation for the same service because of their different judging criteria. A service that is good for some consumers may not be good for other consumers. A global reputation reflects the opinions of the majority consumers. If a consumer happens to be in a minority group, a global reputation of a service may mislead the decision of the consumer. A community-based reputation allows accommodating minority opinions. When a consumer searches for a service using keywords, it often gets a list of services that provide the same functionality. In order to make a good selection, a consumer needs to use its previous experiences with the services and the reputation of the services. The trust in a service reflects a consumer s experiences with the service. If the consumer has good experiences with the service, its trust in the service will be high. The higher the trust in a service, the more likely it is that the consumer will choose the service. If a consumer has no experience with a service, the reputation of the service received from an appropriate super consumer will be used for service selection. Since different super consumers can build different communities based on their interests and judging criteria, different communities may provide different community-based reputation for the same service. A consumer develops trust in different communities based on its trust in their respective super consumers and its trust in the community members. More details about how each consumer computes these different types of trust are given in the next section. If a consumer trusts a community, it can use the community-based reputation for the service. If there is no trusted community, the consumer uses the global reputation for the service instead. As explained above, the reputation management mechanism requires consumers to build three different types of trust and super consumers to build two different types of reputation. Next we will discuss how these different types of trust and reputation are computed. B. Three types of trust built by a consumer A consumer develops three types of trust as follows. 1) Trust in a service All consumers, including super consumers, who are reputation managers of a service compute their own trust in the service they interact with. QoS (Quality of Service) is often used to measure the quality of a service. It has many different metrics, such as response time, accuracy, reliability. The W3C group gives a summarized guide about defining QoS and its metrics [5].The overall evaluation of an interaction between a consumer and a service is a combination of evaluations of each quality metric related to the interaction. How to combine the evaluations of each quality metric depends on the application and a consumer s requirements. For example, for a service providing weather forecast, some consumers may consider the accuracy of the forecast more important. Others may care more about the response time of the service. Yet others may care equally about the two aspects. The result of the overall evaluation, the interaction is satisfying or not satisfying, is used to update a consumer s trust in a service after each interaction according to the formula 1. n o trust = α * trust + (1 α )* eα (1) n trust denotes the trust value after the update; o trust denotes the trust value before the update. α is the learning rate a real number in the interval (0,1). e α is a value of 0, not satisfying or 1, satisfying. Given a threshold tt, a service is trustworthy for a consumer if trust tt. Otherwise, the service is not trustworthy. 2) Trust in a consumer After a consumer interacts with a service, it compares its feedback (e.g. satisfying or not satisfying) for the service with other consumers feedbacks to build trust in each other. The trustworthiness of a consumer from another consumer s perspective is an expression of similarity of the two consumers with respect to their criteria and interests. The formula used to update trust in a consumer is similar to formula (1). If two feedbacks are both satisfying or not satisfying, e equals to 1 and a consumer will increase its trust in the α other consumer. Otherwise, e α equals to 0 and the two consumers decrease their trust in each other. Given a threshold, a consumer is trustworthy if the trust value is greater than the threshold. Otherwise, the consumer is not trustworthy.

4 3) Trust in a community Super consumers can create their communities to provide community based reputations for services. In order to know which community-based reputation is trustworthy to use, a consumer builds trust in a community based on its trust in the super consumer who creates the community and the difference between the number of trustworthy members and the number of untrustworthy members in the community. The algorithm is as follows. trust_c=trust_sc+(trusted_no -nottrusted_no) /a if trust_c > 1, then trust_ c = 1 (2) Here trust_sc is the trust in the community s super-consumer, trusted_no represents the number of trustworthy members in the community. nottrusted_no is the number of untrustworthy members. The value of a is constant and depends on (and is smaller than) the community size. trust_c is the trust in the community. Given a threshold tc, if trust_c > tc, the community is trustworthy. Otherwise, it is not trustworthy. It is clear from the formula (2) that the consumer s trust in the super-consumer plays a big role in the consumer s trust in the community. The reason is simple. A super consumer is the creator of its community. It creates a community that fits its judging criteria. If a consumer trusts a super consumer, it may trust the super consumer s community. If a community has more trustworthy members than untrustworthy members, it is more trusted. C. Reputation built by a super-consumer If a super-consumer serves as a reputation manager for a service, it will build a global reputation for the service. A super consumer can also form a community based on its interests and judging criteria in this case it will also build community based reputation. (1) The global reputation of a web service When a super-consumer serves as a reputation manager for a service, consumers who have utilized the service will send to the super-consumer their feedback about the service. The super-consumer will treat the feedback equally and build a global reputation for the service. The reinforcement learning formula (3) is used to update the service s reputation. n o reputation = β * reputation + (1 β ) * e (3) n reputation is the reputation value after the update; o reputation denotes the reputation value before the update. e β is a value of 0, not satisfying or 1, satisfying. β is the learning rate and between 0 and 1. (2) The community-based reputation The community-based reputation for a web service is the average trust in the service from all the community members as formula (4) shows. If a member happens to have no interaction with a service, a default trust value is used to represent its trust for the service in the formula (4). The default trust value for a web service is the same for all the consumers and services. A super consumer remembers all the trust values from all the members. When a member updates its trust in a service, it will automatically send its updated trust β value to the super-consumer. Then the super -consumer will store the value and use it to update the community-based reputation for the service. In this way, a super consumer passively collects updated information from its members when the members are available and make their updates. reputation i= 1 _ community = g trust g + 1 reputation_community denotes the community-based reputation for a service. trust is the trust value for the service from the i-th community member. g is the number of the community members. A super-consumer will periodically update its member list either based on its trust in a consumer or the reputation of a consumer. If a consumer is trustworthy for the super-consumer, the consumer will be selected as a member. A request for being a community member will be sent to the consumer if the consumer has not been a member previously. It is assumed that a consumer always accepts requests for being a community member. Once a consumer joins a community, it constantly sends the super consumer updates about how it trusts other consumers. The super consumer will store the updates and use them for calculating the reputation of a consumer later, which is the average trust from all the community members. If a consumer s reputation is greater than a specified threshold, the consumer will be selected as a community member. If a community member s reputation falls below the threshold and is not trustworthy to the super consumer, a request for not being a community member will also be sent to the consumer. It will stop sending updates to the super consumer. In our method, each super-consumer is the core of its community, responsible for storing all the community-related information and maintaining the community. Once a super consumer disappears, the information stored in the community will also disappear. In an environment where there are many super-consumers with similar interests, there may be other similar communities available, created by other super consumers with similar interest and judging criteria. When a community fails, a consumer can turn to other communities for information. In order to help each other locate other similar communities, community members also contribute their opinions about how they trust other communities. The super consumer also builds community-based reputation for other communities that have similar interests. A community-based reputation in a given community is computed as the average trust that the community members have developed in that community (computed as shown in section B-3). If a community A has a good community reputation for the members of the current community B, it means that the community A is similar to the current community B in their judging criteria. Therefore, also the community-based reputation for services built in the two communities will be similar. If the current community B becomes unavailable, consumers can turn to the community A for community-based reputation of services. i i (4)

5 III. SIMULATION DESIGN AND RESULTS To evaluate our approach, we developed a simulation. For simplicity, all the services have the same functionality. There are six types of service and four services for each type. Different types of service have different service qualities and prices varying from very low quality and price (Type 1) to very high quality and price (Type 6). There are three groups of consumers, non picky consumers, moderately picky consumers and picky consumers. Picky consumers require higher quality services than moderately picky and non picky consumers. Each group of consumers judge the quality of each type of service differently according to Table 1. For example, for picky consumers, only services in the service type 6 are good. For non picky consumers, almost all the services except the services in the type 1 are good. In the initial state of the experiments, consumers have no knowledge of the service qualities. A best service for a consumer is a service with good quality and low price. For instance, for moderately picky consumers, services in the service type 4, 5 and 6 are all good. But the services in the service type 4 are the best because of their lowest price. Our experiments involve 100 consumers of which 20 are super consumers. There are 3000 interactions in each experiment. We run each experiment for 10 times and use the means for the evaluation criteria. The goal of the first experiment is to see if community-based reputation helps consumers find good services. We have two configurations. In configuration 1, consumers use a combined algorithm utilizing both global reputation and community-based reputation to select a service. In configuration 2, consumers use only global reputation. The distribution of non-picky, moderate picky and picky consumers is 25, 50 and 25, respectively, where the numbers of corresponding super consumers are 5, 10, and 5 respectively. The Y axis represents the rate of successful interactions, which is the ratio of the number of successful interactions over the number of total interactions. A successful interaction is an interaction that a consumer finds satisfying. Fig. 2 shows that the two groups, moderate picky consumers and picky consumers, perform better using the combined algorithm. This is expected since the community-based reputation is inherently tailored for different consumer groups. The non picky consumer group performs almost the same under the two algorithms, which is also expected because almost all the services are good for this group. Therefore the successful interaction rate for non picky consumer group is much higher. The purpose of the second experiment is to measure the robustness of the system. In the system, super consumers are responsible for building reputation, global reputation and community-based reputation. In a dynamic environment, super consumers may not always be available. In the second experiment, we compare the performances of the two systems, one with all super consumers available and the other one with only 50% super consumers available. The numbers of consumers and super consumers for different groups are the same as in the first experiment. Fig. 3 shows that the system can still perform well even with only 50% super consumers Table 1. The service types and judgement Service Type 1: low quality, low price Service Type 2: low quality, low price Service Type 3: low quality, moderate price Service Type 4, moderate quality, moderate price Service Type 5: moderate quality, high price Service Type 6: high quality, high price available. Non Moderately Picky picky picky Bad Bad Bad Good Bad Bad Good Bad Bad Good Good Bad Good Good Bad Good Good Goo d IV. DISCUSSION AND FUTURE WORK Several trust and reputation approaches have been proposed for web service selection. Most of these approaches, unlike ours, depend on a central node to collect and store feedback from consumers and calculate reputation. These approaches differ in their focus and calculation algorithms, e.g. some using collaborative filtering algorithms to select services [7], proposing how to combine different QoS features to get a fair overall rating for a service [6], or how to represent reputation in a semantic way [8]. A decentralized approach for reputation management is proposed in [11]. It uses a specially designed P-Grid structure to organize different reputation managers, which is complex and involves a lot of cost of communication and computation. Trust and reputation mechanisms have been studied in many other areas, like ecommerce, multi-agent systems, and peer to peer systems. These different mechanisms can be classified either as global or as personalized/community-based according to the criteria defined in [12]. In global reputation systems, the reputation of an entity (i.e. a person/agent/product/service) is based on the opinions from the general population, which is public and visible to all the consumers. In personalized/community-based reputation systems, for a requesting consumer, the reputation of an entity is based on the opinions either from a group of consumers chosen by the request consumer (in this case it is called personalized reputation), or from the members of a community, formed by some other criterion (in this case it is called community-based reputation). Most of trust and reputation mechanisms can only build one kind of reputation, either global reputation or personalized/community-based reputation. The method proposed in this paper can support both of kinds of reputation. A global reputation is usually based on a larger population. It can be built faster than a personalized/community-based reputation. Before a consumer can obtain a personalized/community-based reputation, using a global reputation can help a consumer make a selection. But compared with a personalized/community-based reputation,

6 a global reputation is less effective since for a requesting consumer, a personalized/ community-based reputation is built on the opinions of a group of trustworthy consumers, which are more likely to have similar preferences about Interaction Rate Sucesful non moderate picky_combined picky_global moderate picky_global non picky_global picky_combined quality. Therefore, when a personalized/community-based reputation is available, it is better to use it instead of a global reputation Interaction No non moderate picky_available picky_available 100% 100% 50% moderate non picky_available picky_available 50% 50% 100% Fig. 2 The performance of the system using both global and community-based reputation Interaction Rate Sucesful Interaction No Fig 3. The robustness of the system

7 Our method of using super peers to manage reputation combines the characteristics of both centralized and decentralized reputation systems. Building global reputation is easy in a centralized system, but hard in a decentralized system, although several methods have been proposed about how to get global reputation in a decentralized system, such as Eigentrust [3], PeerTrust [13], p-grid [1]. These methods are all designed for a structured P2P network based on DHT or P-Grid. In Eigentrust, every peer has a reputation manager determined by a hash function. The reputation manager is responsible for computing the global reputation for a peer based on the algorithm of power iteration. This method is very complex and also requires the availability of related peers when calculating the global reputation of a peer. PeerTrust [3] introduces several different parameters and factors that can be used to calculate the reputation of peers, and a method of how to combine them. Aberer and Despotovic [1] proposed a complaint-based reputation system. A specially designed P-Grid structure is used to determine reputation managers for peers. A peer s reputation managers are responsible for collecting other peers complaints about the peer. If there are more complaints against a peer, the peer s reputation will be worse. However, our method is designed for an unstructured network, like Gnutella networks or super peer networks, and is simpler. Each service provider is responsible for finding reputation managers (super consumers) for its service and informing its consumers about them. Each reputation manager acts like a central node for a service. It collects feedbacks about the service from all consumers. It also provides consumers with the global reputation of the service when requested. A super consumer can also build its community to bring together consumers with interest and judging criteria similar to its own and form community-based reputation, which is similar to the personalized reputation for a service in some way since the fundament of forming the community is based on the super consumer s interest and judging criteria. However, the community-based reputation is public and serves for a group of consumers with similar interest and judging criteria, while personalized reputation is built for an individual consumer and only visible for this consumer. Our method does not have the drawbacks of centralized or decentralized reputation systems. There can be several super consumers responsible for managing the reputation of a service and each super consumer can be reputation manager for several services. Therefore, there is no problem of single point of failure or scalability. In a decentralized reputation system, like the one proposed in [15], the problem is that a consumer may not be available when another consumer needs its information. The problem may occur in a service-oriented system since consumers have no motivation to stay in the system for a long time. Mostly likely, they enter the system only when they need a service. In our method, super consumers can collect and store the information in individual consumers when they are available. When a consumer becomes a community member, it automatically sends the community s super consumer its updated information about its trust in services and in other consumers. The super consumer will store it and later, even if the consumer is not available, the super consumer can use the stored information to update its community and community trust values. Super consumers play a big role in our method by contributing more resources (e.g. cpu power and bandwidth) to build communities and reputation. In order to make our method work effectively, there must be enough super-consumers in the system. Although it is getting cheaper to have a powerful machine and a high speed network with the advance of technologies, consumers still may not be willing to contribute their resources and to be a super consumer. The phenomenon of free riding in P2P file sharing systems is a good example, where some peers always take resources from others, but do not contribute their resources. How to motivate consumers to become super consumers is a future direction of our work. One solution for this problem could be that providers provide rewards (e.g. discounts for using their services) to super-consumers for being reputation managers for their services. Providers can also reward super-consumers for propagating their reputation by collecting information from their consumers about the super-consumers from whom they know about their services. Other solutions may be possible and need further studies. Our method also involves a potential problem, the collusion between a service provider and its service s reputation managers. In order to prevent this kind of collusion and detect dishonest reputation managers, our mechanism can be extended by letting consumers develop trust in the honesty of super consumers as reputation managers. When super-consumers gossip, they exchange and store not only the information about the reputation of a service, but also information about the reputation manager who provides the reputation of the service. After utilizing a service, a consumer can update its trust in the honesty of the service s reputation manager. If the reputation provided by the reputation manager is good and the result of the interaction is satisfying, the consumer will increase its trust in the honesty of the reputation manager, otherwise decrease its trust. The members of a community can also gossip privately (without the mediation of the super-consumer who created the community) about their trust in the honesty of different reputation managers and develop their own community-based reputation about the honesty of reputation managers. In this way consumers can avoid using reputation managers with a bad reputation and therefore get some protection from web-service provider reputation manager collusion. An important direction for future work is to verify the effectiveness of the method sketched above in detecting and preventing collusion. V. CONCLUSION In this paper, we propose a method of using super consumers to manage reputation. Super consumers can serve as reputation managers for services that are interesting to them, collect feedbacks and build global reputation for these services. Super consumers can also establish their own communities to build community-based reputation that is tailored for consumers with similar to their interests and judging

8 criteria. Our experiments show that the system performs better when using both kinds of reputation and also has good robustness. VI. REFERENCES [1] K Aberer and Z Despotovic, Managing trust in a peer-to-peer information system. in Proceedings of International Conference on Information and Knowledge Management, 2001, pp [2] W. Han, Integrating Peer-to-Peer into Web Services, Master thesis, University of Saskatchewan 2006 [3] D S Kamvar, T M Schlosser, and H Garcia-Molina. The Eigentrust Algorithm for Reputation Management in P2P Networks, in Proceedings of the twelfth International Conference on World Wide Web, 2003, pp [4] F. B. Kashani, C. C. Chen, and C. Shahabi. WSPDS: Web Services Peer-to-Peer Discovery Service, in Proceedings of the International Conference on Internet Computing, 2004, pages [5] K.-C. Lee et al., QoS for Web Services: Requirements and Possible Approaches, World Wide Web Consortium (W3C) note, Nov. 2003; Available online at (last accessed on Nov. 30, 2006): [6] Y. Liu, A. Ngu, and L. Zheng. QoS computation and policing in dynamic web service selection, in Proceedings of the WWW 2004, May [7] U. S. Manikrao, T.V. Prabhakar, Dynamic Selection of Web Services with Recommendation System, in Proceedings of International Conference on Next Generation Web Services Practices, August 2005,. [8] E. M. Maximilien and M. P. Singh. An Ontology for Web Service Ratings and Reputations. In S. Cranefield, T. W. Finin, V. A. M. Tamma, and S. Willmott, editors, Ontologies in Agent Systems, volume 73 of CEUR Workshop Proceedings, 2003, pages [9] S. Saroiu, P. K. Gummadi, S. D. Gribble, A measurement study of peer-to-peer file sharing systems, in Proceedings of Multimedia Computing and Networking (MMCN) [10] L.-H. Vu, M. Hauswirth, K. Aberer, Towards P2P-based Semantic Web Service Discovery with QoS Support, in Proceedings of workshop on Business Processes and Services (BPS), Nancy, France, [11] L.-H. Vu, M. Hauswirth, K. Aberer, QoS-based service selection and ranking with trust and reputation management, in Proceedings of OTM'05, R. Meersman and Z. Tari (Eds.), LNCS 3760, 2005, pages [12] Y. Wang, J, Vassileva. A Review on Trust and Reputation for Web Service Selection, in Proceedings of First International Workshop on Trust and Reputation Management in Massively Distributed Computing Systems (TRAM'2007), in conjunction with ICDCS 2007,June 25-29, 2007, Toronto, Canada [13] L. Xiong and L. Liu, PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities, IEEE Transactions on Knowledge and Data Engineering, Vol.16, No. 7 (July 2004). Special issue on Peer to Peer Based Data Management. pp [14] B. Yang, H. Garcia-Molina, Designing a super-peer network in Proceedings of the 19th International Conference on Data Engineering (ICDE), Bangalore, India, March 2003 [15] B. Yu and P. M. Singh, Distributed Reputation Management for Electronic Commerce, Computational Intelligence, Volume 18, Issue 4, 2002, pages